Being able to interact and communicate with robots in the same way we interact with people has long been a goal of AI and robotics researchers. In this paper, we propose a novel approach to communicating a navigation task to a robot, which allows the user to sketch an approximate map on a PDA and then sketch the desired robot trajectory relative to the map. State information is extracted from the drawing in the form of relative, robot-centered spatial descriptions, which are used for task representation and as a navigation language between the human user and the robot. Examples are included of two hand-drawn maps and the linguistic spatial descriptions generated from the maps.

Current manufacturing methods for robotic-controlled assembly rely on accurate positioning to ensure task completion, often through the use of special fixtures and precise calibration of the workspace. The reliance on precision positioning to achieve proper alignment creates problems in both programming and control of contact-based tasks. As a means of addressing these problems, we have been investigating the use of qualitative contact states (QCS) for modeling and learning low-level, force-based skills. Sensorimotor skills are modeled using force-based discrete states, which describe qualitatively how contact is being made with the environment. The qualitative states can be identified from force signals by viewing them as projected clusters in the force sensor space. In this paper, we investigate the automatic clustering of force data by applying a competitive agglomeration algorithm to extract clusters which can be used for QCS classifier training. Experimental results are included using an automotive transmission assembly.